Correcting digital images using unique subjects

ABSTRACT

A method and apparatus for correcting digital images is provided. In one embodiment of the invention, a unique subject in a current image to be corrected is identified in one or more reference images. Pixel characteristics of the unique subject in the current image to be corrected are compared to the pixel characteristics of the unique subject as it appears in the one or more reference images. Using the comparison, a systemic error of the current image is inferred, and a correction function correcting the inferred error is determined. One embodiment of the invention corrects the color of a digital image based on identifying individual humans in the current image and in the reference images.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] An embodiment of the present invention applies to the field ofdigital photography and, in particular, to correcting digital images.

[0003] 2. Description of the Prior Art

[0004] Digital photography is the process of capturing, processing, andrendering images digitally. Digital representation of images, whichmeans that images are represented by a finite number of pixels (PictureElements) where each pixel can assume a finite number of values, is wellknown in the art. Capturing digital images by using, for example,digital cameras or scanners is also well known. One advantage of digitalphotography over traditional analog photography is that ageneral-purpose computer, or specialized circuit, can process the imagescaptured digitally. Such processing may include color correction, redeyeremoval, intentional distortion to create “special” effects, andchanging or correcting various other characteristics of the image.

[0005] One characteristic of a digital image that may need correcting iscolor. Color correcting involves a user manipulating the image, with orwithout automatic calculations by a computer, to make the colors on theimage closer to what the user is looking for. There are severalcolor-correcting schemes known in the art.

[0006] One such scheme requires that the user identify an object with aknown color-range in the image to be corrected. For example, the usercould use a mouse to click on a portion of a digital image representinga person. The computer would then compare the skin tone colors of thatperson to a color-range known for human skin, that is, the usual rangeof colors skin takes on. For example, if skin appears blue in an image,the computer—or the person performing the correction by hand—will knowthat corrections must occur. Other schemes relying on generalinformation about what certain objects, such as humans, sky, plants, andso on also exist. For example, the product known as iCorrect produced byPictograpics uses this general type of technique.

[0007] One limitation of these techniques is that they rely on a rangeof colors to perform the correction, and are thus limited in accuracy bythis range. For example, it is true that skin cannot be blue, but it canbe a broad range between pale white and dark black. Even excludingpeople of African descent, it is clear that relying on the range ofexpected skin colors for people, or other general objects, is limited inaccuracy.

[0008] However, the range of colors for a specific person, or specificobject, does not vary quite as much. For example, the skin tone of anindividual varies much less than the skin range within even a singleethnicity of people. Furthermore, most of the variation in color forindividual people is seasonal. The prior art schemes have not exploitedthis, and no correction scheme relies on specific and unique subjects inimages, such as a specific person, or a specific tree, for digital imagecorrection.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The invention may best be understood by referring to thefollowing description and accompanying drawings that are used toillustrate embodiments of the invention. Embodiments of the presentinvention are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements and in which:

[0010]FIG. 1 is an exemplary computer system on which embodiments of thepresent invention may be practiced.

[0011]FIG. 2 is a representation of digital images that may be used byvarious embodiments of the present invention.

[0012]FIG. 3 is a flow chart illustrating digital image correctionprocessing according to one embodiment of the present invention.

[0013]FIG. 4 is an exemplary representation of a transfer function curvefor one channel as it might appear in one embodiment of the presentinvention.

DETAILED DESCRIPTION

[0014] In general, embodiments of the present invention exploit the factthat unique picture subjects, such as people, should have the samecolor, or other picture characteristics, from one image to the next.Thus, the fact that skin tones of individual people are generallyconsistent is used to correct a digital image. Some embodiments of thepresent invention use facial recognition technology and image archivingto automate various aspects of the correction process.

[0015] A digital image is represented by a matrix of pixels. Each pixelis associated with a number representing the brightness intensity of thepixel. Each pixel is represented by a set of bits in memory, and is ableto take on various discrete brightness levels. Digital images generallyuse three or four bytes to represent a colored pixel, with each byterepresenting the intensity of a channel. Each channel is a differentcolor or other measurement needed to express color. How many channels apixel has and what the channels represent depends on the colorspace usedby the color representation scheme.

[0016] For example, the RGB (red-green-blue) colorspace uses three colorchannels. In the RGB colorspace the color of each pixel is determined bythe intensity of the red, green, and blue channels in each pixel.Generally, three bytes are used to store this information—one byte perchannel—although more or less memory may be used depending on how manydiscrete intensities are desired for each color channel. Using one byteper channel results in each channel being able to assume 256 discretebrightness levels.

[0017] To represent a shade of purple in the RGB colorspace, theintensity of each channel—i.e. color component—would be set so that thecombined visual output on rendering would appear purple to the healthyhuman eye. For purple, the intensity of the red and blue channels willlikely be greater than the green channel. For example, the shade ofpurple may be represented as channel intensities (200, 50, 220).

[0018] Such a specific color is known as a vector in the colorspace.Other colorspaces, such as the HSV (hue-saturation-value) colorspace andthe CMYK (cyan-magenta-yellow-black) colorspace, are also known in theart. Embodiments of the invention are thus applicable to any colorspace,known or yet to be developed. However, for simplicity, most of thediscussion below will assume using the RGB colorspace.

[0019] Since the color of a pixel is represented by a vector in thecolorspace, the color of a pixel will be hereinafter treated as a singlevalue, such as an m-vector. In the case of three channels per coloredpixel with one byte of memory per channel, each pixel could take on 256(16,777,216) discrete colors. One skilled in the art understands thatadjusting or correcting color means changing the color values forindividual colored pixels from one color among the over 16 millionpossibilities of the colorspace, to another color, according to somescheme or technique.

[0020] A computer system in which features of the present invention maybe implemented will now be described with reference to FIG. 1. In oneembodiment of the present invention, computer system 100 may be apersonal computer. In alternate embodiments of the invention, certainfeatures of the computer system 100 needed to carry out embodiments ofthe invention may be incorporated into a specialized device, such as adigital camera, a scanner, or any other digital device. In yetalternative embodiments of the invention, certain aspects of the presentinvention may be carried out on a specialized device while other aspectsmay be carried out on a general purpose computer coupled to the device.In another embodiment of the invention, computer system 100 may beresident on a specialized device, such as a digital camera.

[0021] Computer system 100 comprises a bus or other communication means101 for communicating information, and a processing means such asprocessor 102 coupled with bus 101 for processing information. In oneembodiment of the invention, the tasks performed to practice embodimentsof the invention as performed by the processor 102 either directly orindirectly.

[0022] Computer system 100 further comprises a random access memory(RAM) or other dynamic storage device 104 (referred to as main memory),coupled to bus 101 for storing information and instructions to beexecuted by processor 102. Main memory 104 also may be used for storingtemporary variables or other intermediate information during executionof instructions by processor 102. In one embodiment of the invention,images accessed by the invention are stored digitally on the main memory104 during certain intervals while the invention is practiced. Computersystem 100 also comprises a read only memory (ROM) and/or other staticstorage device 106 coupled to bus 101 for storing static information andinstructions for processor 102.

[0023] A data storage device 107 such as a magnetic disk or optical discand its corresponding drive may also be coupled to bus 101 for storinginformation and instructions. In one embodiment of the invention, imagesaccessed by the invention are stored on a data storage device 104 duringcertain intervals while the invention is practiced. Computer system 100can also be coupled via bus 101 to a display device 121, such as acathode ray tube (CRT) or Liquid Crystal Display (LCD), for displayinginformation to a computer user. In one embodiment of the invention, theimages accessed by the invention are displayed on the display device121, and the color correcting occurs in the colorspace used by thedisplay device 121. Computer system 100 can also be coupled via bus 101to a printing device 124, such as a laser printer, or any other printer.The printer 124 may be a color or a black and white printer. In oneembodiment of the invention, the images accessed by the invention mayrendered on the printer 124, and the color correcting may be implementedin the printer.

[0024] Typically, an alphanumeric input device 122, includingalphanumeric and other keys, may be coupled to bus 101 for communicatinginformation and/or command selections to processor 102. Another type ofuser input device is cursor control 123, such as a mouse, a trackball, ajoystick, or cursor direction keys for communicating directioninformation and command selections to processor 102 and for controllingcursor movement on display 121.

[0025] A communication device 125 is also coupled to bus 101 foraccessing remote servers or other servers via the Internet, for example.The communication device 125 may include a modem, a network interfacecard, or other well-known interface devices, such as those used forcoupling to an Ethernet, token ring, or other types of networks. In anyevent, in this manner, the computer system 100 may be coupled to anumber of clients and/or servers via a conventional networkinfrastructure, such as a company's Intranet and/or the Internet, forexample. In one embodiment of the invention, images accessed by theinvention are stored digitally on the Internet, or any machineaccessible by communication device 125 during certain intervals whilethe invention is practiced. In another embodiment of the invention,images accessed by the invention are stored on a specialized device,such as a digital camera, coupled to the bus 101 via an input/outputport 126 during certain intervals while the invention is practiced.Embodiments of the invention are applicable to all digital imagesregardless of the medium used for their storage.

[0026] Demonstrative Example

[0027] The following embodiments of the present invention are set forthto demonstrate the present invention. In these embodiments, theinvention is implemented by focusing on the uniqueness of humans incorrecting the color of digital images. These embodiments of theinvention are used to correct the color of an image 202 in FIG. 2. Theimage 202 may have been captured by a digital camera, or any othermeans, and may be stored in a memory element, such as main memory 104,or fixed in some other medium. Image 202 depicts Harry 233, Dick 224,and Jane 225, three hypothetical humans. In FIG. 2, only representationsof the three people are used. In an actual image, it is contemplatedthat the three people could be sharing the same physical space andinteracting, like people in most photographs do.

[0028] Jane 225 may be identified as a unique human individual using aknown, or yet to be developed, facial-recognition technique or scheme.Generally, the facial recognition process will have access to anotherimage, or several images, in which Jane 225 is identified as such, butthis may not be necessary with all facial recognition schemes. Forexample, the recognition scheme may be preprogrammed with Jane's uniquerecognizable characteristics. Alternatively, a user may manuallyidentify Jane 225 in the image 202, for example by clicking a mouse onJane's face and associating the input with the word “Jane” in adatabase.

[0029] Once Jane 225, or any other person in the image to be correctedwho is recognizable by the scheme, is identified, a search for Jane 225in the library of images 204 may be performed. These images 204 may bestored in main memory 104 or read only memory 106 and accessible byprocessor 102. That is, using a facial recognition technique,presumptively color-correct images depicting Jane 225 that may be usedas a reference in color correcting may automatically be found.

[0030] In FIG. 2 these images 206 containing Jane 225 show onlyrepresentations of Jane. In an actual image library, it is contemplatedthat these images 206 may include Jane in a variety of settings with avariety of other people and objects. Furthermore, a user may manuallychoose the images 206 from the image library 204 that depict Jane 225,and manually identify Jane 225 within those images.

[0031] Once previous instances of Jane 225 depicted in images other thanthe image 202 are identified and selected, one or more depictions ofJane 225 that are presumptively color-correct are chosen, and the colorof Jane's skin color is calculated. If more than one reference images206 are chosen, the calculated skin color may be an average skin color.This average may be a weighted average based, for example, on picturequality, user input, the capture date of the images, or some otherfactor.

[0032] The selection of appropriate reference images may be based on acomparison of the dates of the reference images with the date of theimage 202 to be corrected, if available. For example, if the image 202to be corrected was captured in June, and the three images 206 in whichJane 225 appears are dated from December, January and Augustrespectively, then using the August image may result in more accuratecolor correction, since it is likely that Jane's skin tone is differentin the summer than in the winter. Thus, the selection or weighing of thereference images may be based on the capture dates of the respectiveimages.

[0033] If a single reference image is used, adjustments may be made tothe reference skin tone based on a comparison of the capture dates ofthe reference image and the image to be corrected. In some embodimentsof the invention, a skin sample database may be kept to track seasonalchanges in individuals used in color correction. Iterative methods mayalso be used to guess at skin tones between known extremes for certainindividuals. While these approaches use skin tone ranges, these rangesare for specific individuals, not for all people in general.

[0034] In one embodiment of the invention, Jane's skin color may becalculated by selecting a subset of pixels used in depicting Jane 225 inthe current image to be corrected 202 and averaging the color values ofthose pixels. Care should be taken that pixels used in this calculationall depict Jane's skin. To do this, statistical outliers values in colormay be discarded. Furthermore, the pixel selection scheme may use facialdata gathered by the facial recognition scheme to ensure that the pixelsselected are not from eyes, lips, facial hair, or other non-skin coloredaspects of the human face. Also, these pixels may exclude places on theface where women are likely wear makeup, such as the cheek. Where theselection of the subset is manual, the user must exercise this care.This calculated skin color is usually expressed as a vector in thecolorspace.

[0035] Once Jane's presumptively correct skin color is calculated fromthe reference image or images, it is compared with Jane's skin color inthe image 202 to be corrected. Jane's skin color in this image 202 maybe calculated in any of the ways described above, or according to yetanother technique. The comparison may then be used to correct the image202. The comparison may include calculating a scalar correction vector,which when multiplied by the color vector representing Jane's skin tonein the image to be corrected 202 results in the color vectorrepresenting Jane's skin tone in the reference image. Mathematicallythis may be expressed as:

C _(p) * V=C _(r) where

[0036] C_(p) is the color vector in the colorspace calculated for Jane'sskin in the image to be corrected;

[0037] C_(r) is the color vector in the colorspace calculated for Jane'sskin in the image to be corrected;

[0038] V is the scalar correction vector; and

[0039] represents vector multiplication in the colorspace, i.e. thevector space. Since C_(p) and C_(r) are known after the calculationsexplained above, determining the correction vector in one embodiment ofthe invention can be done by:

[0040] V=C_(r)/C_(p) where/represents vector division in the colorspace.

[0041] More sophisticated comparison schemes may be used. Instead ofusing a static correction vector, a transfer function may be determinedfor each channel to make a correction function. A transfer function isnot a scalar multiplier like the correction vector, but is dependent onthe intensity of the input. A transfer function maps the range ofintensity values for a color channel into the range of intensity valuesfor that channel.

[0042] For example, let T_(r)(x)=y be the transfer function for red inan RGB colorspace, where the subscript r denotes the red color channel,x is a input intensity value (between 0 and 255 if one byte per channelper pixel is used), and y is the output intensity value (with the samerange as x). While the values for x and y are discrete in digitalphotography, the transfer function can be described visually by a curve,which may look like the graph in FIG. 4.

[0043]FIG. 4 is a function graph 400 of equation 402, the equation forthe red transfer function. The graph 400 includes a horizontal (x) axisthat represents the input intensities, 0 to 255 in this embodiment ofthe invention. The graph 400 also includes a vertical (y) axis thatrepresents the output intensities that have the same range as the inputintensities. Generally the input and output ranges will be identical,but they do not have to be. The function T_(r)(x) is graphed as curve410.

[0044] As is understood by one skilled in the art, curve 410 determinesthe output (y) value an input (x) value in mapped to. For example, asshown in FIG. 4, an input intensity of 150 (x=150) is mapped to anoutput intensity of 100 (y=100). This is because T_(r)(150)=100. It isfurther understood by one skilled in the art, that the correction vectorset forth above is a specific implementation of the transfer function inwhich the transfer functions are limited to linear functions, i.e.straight lines only for curve 410.

[0045] It is further understood by one skilled in the art, that pointsalong the diagonal 412 for any curve 410 mean that the transfer functionleaves those values unaltered. Curve 412 has no such values, except for0 and 255. However, curve 412 may be any curve, including one withnumerous, or even all, points on the diagonal 412. It is furtherunderstood by one skilled in the art, that curve 412 is merely a visualrepresentation of the discrete-valued transfer function T_(r)(x). How tocalculate transfer functions from the pixels in the image to becorrected and the pixels in the reference image selected forimage-correction would be apparent to one skilled in the art, andvarious known and yet to be developed methods may be used withoutdeviating from the scope of the invention. Transfer functions for allother color channels of the colorspace can be calculated in a similarmanner.

[0046] The transfer functions, correction vector, or other result of thecomparison may then be used to correct the entire image 202.Alternatively, only corrects a subset of the image 202, for example onlyJane's skin, may be corrected. The actual correction may be performed bymultiplying the color vector of each pixel to be corrected by thecorrection vector. The correction may also be performed by applying thetransfer functions to each channel of each pixel. Some other embodimentsof the invention may use different correction techniques based on theway the correction function was obtained.

[0047] If more than one human is depicted in an image 202 to becorrected, like Harry 223 and Dick 224, their identities and skin tonesmay also be used for color correction in the same manner as describedfor Jane. Thus, alternate color-corrections for the image 202 may beproduced, each alternative based on the skin tone of a different humanindividual depicted in both the image 202 to be corrected and in someimage in the image library 204. Then, the user may choose a preferredcorrected image. Alternatively, further processing, such as anotherknown color-correction technique, may be used in combination toautomatically determine the best color-correction result. Additionally,a combined correction function or vector may be determined from theindividual correction functions based on each individual person.

[0048] Digital Image Correction

[0049] Digital image correction, according to some embodiments of theinvention, is described more generally with reference to FIG. 3. Thedesign of the flow chart does not necessarily imply a temporal orchronological relation between all blocks of the flow chart, it ismerely to aid in understanding the embodiments of the inventiondescribed in relation thereto.

[0050] In 302, an image to be corrected is received. The image to becorrected is a digital image made up of colored pixels, where the colorof each pixel is represented by a vector in a colorspace. For example,in the RGB colorspace with one byte per channel, a pure blue pixel wouldbe represented by the vector (0,0,256) where the numbers represent theintensity of the channels. In 304, a unique subject in the image to becorrected is identified. This may be accomplished by some user input,such as selecting a chair and identifying it as “Grandma's chair.” Ifthe unique subject to be identified is a person, then the identificationmay be automated by using a facial recognition scheme. Other uniquesubjects, such as chairs, trees, shirts, and so on, may also beidentified automatically. Embodiments of the invention are applicable toany automated identification technique now existing, or to be developedin the future.

[0051] In 306, a pixel characteristic for the identified unique subjectis determined. For color correction, this can be accomplished byaveraging the colors of a number of pixels that are used to depict theunique subject in the image to be corrected. This sample set of pixelsmay be user selected, or may be selected automatically building on theobject recognizing technique. For example, in the case of facerecognition, the technique can select a subset of pixels of the facewhere skin color is likely to be found. Several error control schemesmay be used to ensure that the selected subset of pixels is indeed skincolored.

[0052] In other embodiments of the invention, the pixel characteristicmay be more complex then color. For example, the pixel characteristic tobe corrected may be pixel texture, which may not be expressed as asingle vector or value. Modifications to embodiments of the presentinvention are apparent to those skilled in the art that would make itapplicable to pixel characteristics other than color, so long as aunique reference subjects are used.

[0053] In 312, one or more reference images containing the uniquesubject are selected. This may be performed manually by the user. Forexample, the user who selected “Grandma's chair” in the image to becorrected may then select another image in which Grandma's chairappears, and identify, in 314, the chair within the image using someinput mechanism such as cursor control device 123.

[0054] If a facial recognition scheme is used, reference imagesincluding the person identified in the image to be corrected can befound automatically. These images may then be displayed to the user fora selection of one or more of the possible reference images, or thisselection may be automated as well. A date comparison mechanism may alsobe used here to select the most appropriate reference image. In 316, thereference pixel characteristic is calculated. This may be performedsimilarly to the pixel characteristic calculation for the unique subjectdiscussed above, but using the reference image.

[0055] If multiple reference images are used some combining method, suchas averaging, may be used to achieve a more accurate reference pixelcharacteristic. For example, if the unique subject is a person, andthere are ten possible reference pictures, but the person was sick intwo and very tan in one, the average skin color of the person will morelikely resemble the accurate skin color for that person. A datecomparison mechanism may also be used to aid in weighing or discardingreference images in embodiments of the invention where multiplereference images are used. User override of automated decisions can beimplemented for all aspects of various embodiments of the invention toprotect against errors.

[0056] When both the pixel characteristic from the image to be correctedand the reference pixel characteristic from one of more reference imagesare determined, they are used to infer a systemic error in the image tobe corrected 320. A systemic error may be one that occurs in portions ofthe image to be corrected, or perhaps in the entire image. In the caseof a color correction, this inference may be that the color of the imageto be corrected is different from the color the image should have in thesame proportion as the color of the unique subject in the image to becorrected is different from the reference color in each color channel.This comparison may then result in the correction vector or thecorrection transfer functions. Other ways of comparing the two pixelcharacteristic measurements and deriving an error function for the imageto be corrected depending on speed and quality requirements in additionto the characteristic to be corrected for would be apparent to thoseskilled in the art.

[0057] In 322, the inferred systemic error is applied to the image to becorrected. In one embodiment of the invention, this may mean applyingthe transfer functions to each color channel in each pixel making up theimage to be corrected. The correction may entail multiplying the colorof each pixel in the image by the correction vector. The correction mayonly be applied to a subset of the pixels in the image to be corrected.For example, if the user is only unhappy with the color of the uniquesubject, the correction could be limited to those pixels used torepresent the unique subject. Another way of accomplishing this resultis to alter the transfer functions in such a way that applying thetransfer functions on unselected portions of the image to be correctedwill leave the color of that portion unaltered. Visually, the curve ofeach transfer function for each channel will cross the diagonal for thevalues representing the unselected portions to be unaltered.

[0058] Alternatively, if the user is only happy with the color of acertain portion of the image to be corrected, such as the sky, the usermay select this portion to be excluded from the color correction.Alternatively, this exclusion may alter the transfer functions in such away that applying the transfer functions to the selected portion willleave the color of that portion unaltered. Visually, the curve of eachtransfer function for each channel will cross the diagonal for thevalues representing the portions to be unaltered.

[0059] General Matters

[0060] In the description above, for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of embodiments of the present invention. It will beapparent, however, to one skilled in the art that embodiments of thepresent invention may be practiced without some of these specificdetails. In other instances, well-known structures and devices are shownin block diagram form.

[0061] Embodiments of the present invention include various processes.The processes of the embodiments of the present invention may beperformed by hardware components, or may be embodied inmachine-executable instructions, which may be used to cause ageneral-purpose or special-purpose processor or logic circuitsprogrammed with the instructions to perform the processes.Alternatively, the processes may be performed by a combination ofhardware and software.

[0062] Embodiments of the present invention may be provided as acomputer program product, which may include a machine-readable mediumhaving stored thereon instructions, which may be used to program acomputer or other electronic devices including but not limited todigital cameras, scanners, and other digital image capture devices, andprinters and other digital rendering devices—to perform a processaccording to an embodiment of the present invention. Themachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs,RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or othertype of media/machine-readable medium suitable for storing electronicinstructions. Moreover, embodiments of the present invention may also bedownloaded as a computer program product, wherein the program may betransferred from a remote computer to a requesting computer by way ofdata signals embodied in a carrier wave or other propagation medium viaa communication link (e.g., a modem or network connection).

[0063] Various embodiments of the present invention have been describedabove in the context of color correcting digital images and illustratedwith the specific example of how the invention would work with a facialrecognition scheme. However, the invention is not limited to colorcorrection or to facial recognition. The invention instead applies tocorrecting any characteristic of a digital image by comparing the pixelcharacteristics of a unique subject with those of the same subject in adifferent reference image. Furthermore, the present invention is notlimited to digital photography, but is also applicable to digital video,or other digital moving image technology, as would be apparent to oneskilled in the art. Also, the present invention related to theprocessing of digital images, and is therefore equally applicable toprocesses where aspects of image capture, rendering, or both areperformed using analog methods. Images corrected using an embodiment ofthe present invention may also undergo additional processing, digital orotherwise. Such processing also does not make the present inventioninapplicable.

[0064] While the invention has been described in terms of severalembodiments, those skilled in the art will recognize that the inventionis not limited to the embodiments described, but can be practiced withmodification and alteration within the spirit and scope of the appendedclaims. The description is thus to be regarded as illustrative insteadof limiting.

What is claimed is:
 1. A method for correcting images comprising:receiving information identifying a unique subject in a current image,the current image including a plurality of pixels; determining a currentpixel characteristic using a subset of pixels from the unique subject inthe current image; receiving a reference pixel characteristic for theunique subject; inferring a systemic error of the current image bycomparing the current pixel characteristic with the reference pixelcharacteristic; and correcting pixel characteristics of a correctionsubset of the current image based, at least in part, on the inferredsystemic error, the correction subset being a subset of the plurality ofpixels.
 2. The method of claim 1, wherein the unique subject is a human.3. The method of claim 2, wherein receiving information identifying theunique subject comprises using a facial recognition scheme toautomatically identify the human.
 4. The method of claim 1, whereinreceiving the current pixel characteristic comprises selecting thesubset of pixels from the unique subject in the current image; andcalculating a pixel characteristic for each pixel in the subset ofpixels.
 5. The method of claim 4, wherein receiving the current pixelcharacteristic further comprises combining the calculated pixelcharacteristics.
 6. The method of claim 1, wherein receiving thereference pixel characteristic comprises selecting a subset of pixelsfrom the unique subject in a reference image; and calculating a pixelcharacteristic for each pixel in the subset of pixels.
 7. The method ofclaim 6, wherein receiving the reference pixel characteristic furthercomprises combining the calculated pixel characteristics.
 8. The methodof claim 1, wherein the pixel characteristic comprises color, the colorof a pixel being represented by a vector in a colorspace.
 9. The methodof claim 2, wherein the pixel characteristic comprises skin color of thehuman, the skin color being represented by a vector in a colorspace. 10.The method of claim 6, further comprising using an automated recognitionscheme to automatically select the reference image that includes theunique subject.
 11. The method of claim 10, wherein the automatedrecognition scheme is a facial recognition scheme and the unique subjectis a human.
 12. The method of claim 1, wherein receiving the referencepixel characteristic comprises selecting a plurality of subsets ofpixels from the unique subject in a plurality of reference images;calculating a pixel characteristic for each pixel in each subset ofpixels; and combining the calculated pixel characteristics.
 13. Themethod of claim 1, wherein the correction subset comprises the pluralityof pixels comprising the current image.
 14. The method of claim 2,wherein receiving the reference pixel characteristic comprises selectinga plurality of reference images including the human, comparing a capturedate of the current image with capture dates of the reference images;selecting a subset of reference images based on the proximity of thecapture dates of the reference images to the capture date of the currentimage; selecting a plurality of subsets of pixels from the uniquesubject in the subset of the plurality of reference images; calculatinga pixel characteristic for each pixel in each subset of pixels; andcombining the calculated pixel characteristics.
 15. The method of claim6, further comprising, comparing a capture date of the current imagewith capture dates of the reference image.
 16. The method of claim 12,wherein the combining comprises a weighted combining.
 17. Amachine-readable medium having stored thereon data representingsequences of instructions that, if executed by a processor, cause theprocessor to: receive information identifying a unique subject in acurrent image, the current image including a plurality of pixels;determine a current pixel characteristic using a subset of pixels fromthe unique subject in the current image; receive a reference pixelcharacteristic for the unique subject; infer a systemic error of thecurrent image by comparing the current pixel characteristic with thereference pixel characteristic; and correct pixel characteristics of acorrection subset of the current image based, at least in part, on theinferred systemic error, the correction subset being a subset of theplurality of pixels.
 18. The machine-readable medium of claim 17,wherein the unique subject is a human.
 19. The machine-readable mediumof claim 18, wherein receiving information identifying the uniquesubject comprises using a facial recognition scheme to automaticallyidentify the human.
 20. The machine-readable medium of claim 17, whereinreceiving the current pixel characteristic comprises selecting thesubset of pixels from the unique subject in the current image; andcalculating a pixel characteristic for each pixel in the subset ofpixels.
 21. The machine-readable medium of claim 20, wherein receivingthe current pixel characteristic further comprises combining thecalculated pixel characteristics.
 22. The machine-readable medium ofclaim 17, wherein receiving the reference pixel characteristic comprisesselecting a subset of pixels from the unique subject in a referenceimage; and calculating a pixel characteristic for each pixel in thesubset of pixels.
 23. The machine-readable medium of claim 22, whereinreceiving the reference pixel characteristic further comprises combiningthe calculated pixel characteristics.
 24. The machine-readable medium ofclaim 17, wherein the pixel characteristic comprises color, the color ofa pixel being represented by a vector in a colorspace.
 25. Themachine-readable medium of claim 18, wherein the pixel characteristiccomprises skin color of the human, the skin color being represented by avector in a colorspace.
 26. The machine-readable medium of claim 22,wherein receiving the reference pixel characteristic further comprisesusing an automated recognition scheme to automatically select thereference image that includes the unique subject.
 27. Themachine-readable medium of claim 26, wherein the automated recognitionscheme is a facial recognition scheme and the unique subject is a human.28. The machine-readable medium of claim 17, wherein receiving thereference pixel characteristic comprises selecting a plurality ofsubsets of pixels from the unique subject in a plurality of referenceimages; calculating a pixel characteristic for each pixel in each subsetof pixels; and combining the calculated pixel characteristics.
 29. Themachine-readable medium of claim 17, wherein the correction subsetcomprises the plurality of pixels comprising the current image.
 30. Themachine-readable medium of claim 18, wherein receiving the referencepixel characteristic comprises selecting a plurality of reference imagesincluding the human, comparing a capture date of the current image withcapture dates of the reference images; selecting a subset of referenceimages based on the proximity of the capture dates of the referenceimages to the capture date of the current image; selecting a pluralityof subsets of pixels from the unique subject in the subset of theplurality of reference images; calculating a pixel characteristic foreach pixel in each subset of pixels; and combining the calculated pixelcharacteristics.
 31. The machine-readable medium of claim 22, whereinreceiving the reference pixel characteristic further comprises comparinga capture date of the current image with capture dates of the referenceimage.
 32. The machine-readable medium of claim 28, wherein thecombining comprises a weighted combining.
 33. An apparatus comprising: abus; at least one memory coupled to the bus, the at least one memorystoring a current image, at least one reference image, and instructions;a processor coupled to said bus, the processor operable to execute theinstructions, the instructions causing the processor to: identify aunique subject in the current image, the current image including aplurality of pixels; determine a current pixel characteristic using asubset of pixels from the unique subject in the current image; determinea reference pixel characteristic using a subset of pixels from theunique subject in the at least one reference image; infer a systemicerror of the current image by comparing the current pixel characteristicwith the reference pixel characteristic; and correct pixelcharacteristics of a correction subset of the current image based, atleast in part, on the inferred systemic error, the correction subsetbeing a subset of the plurality of pixels.
 34. The apparatus of claim33, wherein the correction subset comprises the plurality of pixelscomprising the current image.
 35. The apparatus of claim 33, wherein thepixel characteristic comprises color in a colorspace.
 36. The apparatusof claim 33, wherein the unique subject in a unique human.
 37. Theapparatus of claim 36, wherein the pixel characteristic of the uniquehuman comprises skin tone.
 38. The apparatus of claim 36, wherein theprocessor identifies a unique subject in the current image by using afacial recognition scheme.
 39. The apparatus of claim 36, wherein theprocessor selects the at least one reference image from a plurality ofimages by using a facial recognition scheme.
 40. A method forcolor-correcting a current image comprising: identifying a unique personin the current image by using an automated facial recognition scheme;selecting a reference image from a plurality of stored images, thereference image including the identified person, by using an automatedfacial recognition scheme; determining a current skin color of theperson in the current image; determining a reference skin color of theperson in the reference image; calculating a color correction functionby comparing the determined current skin color with the determinedreference skin color; and color-correcting the current image using thecolor correction function.
 41. The method of claim 40, wherein the colorcorrection function comprises a plurality of transfer functions, thetransfer functions each determining the color correction for a colorchannel in a colorspace.
 42. The method of claim 40, further comprising:selecting a second reference image including the identified person usingan automated facial recognition scheme; determining a second referenceskin color of the person in the second reference image; calculating thereference skin color by combining the skin colors from the referenceimages.
 43. The method of claim 40, further comprising: selecting asecond reference image including the identified person using anautomated facial recognition scheme; comparing a capture date of thereference image with a capture date of the current image; comparing acapture date of the second reference image with the capture date of thecurrent image; selecting at least on of the reference images based onthe comparison.